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What does variation in survey design reveal about the nature of measurement errors in household consumption ?

Author

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  • Gibson, John
  • Beegle, Kathleen
  • De Weerdt, Joachim
  • Friedman, Jed

Abstract

This paper uses data from eight different consumption questionnaires randomly assigned to 4,000 households in Tanzania to obtain evidence on the nature of measurement errors in estimates of household consumption. While there are no validation data, the design of one questionnaire and the resources put into its implementation make it likely to be substantially more accurate than the others. Comparing regressions using data from this benchmark design with results from the other questionnaires shows that errors have a negative correlation with the true value of consumption, creating a non-classical measurement error problem for which conventional statistical corrections may be ineffective.

Suggested Citation

  • Gibson, John & Beegle, Kathleen & De Weerdt, Joachim & Friedman, Jed, 2013. "What does variation in survey design reveal about the nature of measurement errors in household consumption ?," Policy Research Working Paper Series 6372, The World Bank.
  • Handle: RePEc:wbk:wbrwps:6372
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    References listed on IDEAS

    as
    1. Beegle, Kathleen & De Weerdt, Joachim & Friedman, Jed & Gibson, John, 2012. "Methods of household consumption measurement through surveys: Experimental results from Tanzania," Journal of Development Economics, Elsevier, vol. 98(1), pages 3-18.
    2. John Gibson & Bonggeun Kim, 2010. "Non-Classical Measurement Error in Long-Term Retrospective Recall Surveys," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(5), pages 687-695, October.
    3. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
    4. Angus Deaton & Christina Paxson, 1998. "Economies of Scale, Household Size, and the Demand for Food," Journal of Political Economy, University of Chicago Press, vol. 106(5), pages 897-930, October.
    5. Pischke, Jorn-Steffen, 1995. "Measurement Error and Earnings Dynamics: Some Estimates from the PSID Validation Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 305-314, July.
    6. Shahidur R. Khandker, 2005. "Microfinance and Poverty: Evidence Using Panel Data from Bangladesh," World Bank Economic Review, World Bank Group, vol. 19(2), pages 263-286.
    7. Andrew Chesher & Christian Schluter, 2002. "Welfare Measurement and Measurement Error," Review of Economic Studies, Oxford University Press, vol. 69(2), pages 357-378.
    8. Gibson, John, 2002. " Why Does the Engel Method Work? Food Demand, Economies of Size and Household Survey Methods," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(4), pages 341-359, September.
    9. Alderman, Harold & Hoogeveen, Hans & Rossi, Mariacristina, 2006. "Reducing child malnutrition in Tanzania: Combined effects of income growth and program interventions," Economics & Human Biology, Elsevier, vol. 4(1), pages 1-23, January.
    10. John Gibson & Bonggeun Kim, 2007. "Measurement Error in Recall Surveys and the Relationship between Household Size and Food Demand," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(2), pages 473-489.
    11. Naeem Ahmed & Matthew Brzozowski & Thomas Crossley, 2006. "Measurement errors in recall food consumption data," IFS Working Papers W06/21, Institute for Fiscal Studies.
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    Citations

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    Cited by:

    1. Francisco G. Ferreira & Nora Lustig & Daniel Teles, 2015. "Appraising cross-national income inequality databases: An introduction," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 13(4), pages 497-526, December.
    2. Zezza, Alberto & Federighi, Giovanni & Adamou, Kalilou & Hiernaux, Pierre, 2014. "Milking the data : measuring income from milk production in extensive livestock systems -- experimental evidence from Niger," Policy Research Working Paper Series 7114, The World Bank.
    3. repec:eee:jfpoli:v:72:y:2017:i:c:p:94-111 is not listed on IDEAS
    4. Leandro De Magalhães & Raül Santaeulàlia-Llopis, 2015. "The Consumption, Income, and Wealth of the Poorest: Cross-Sectional Facts of Rural and Urban Sub-Saharan Africa for Macroeconomists," Bristol Economics Discussion Papers 15/655, Department of Economics, University of Bristol, UK.
    5. Jed Friedman & Kathleen Beegle & Joachim De Weerdt & John Gibson, 2016. " Decomposing response error in food consumption measurement: implications for survey design from a survey experiment in Tanzania," Working Papers LICOS Centre for Institutions and Economic Performance 537166, KU Leuven, Faculty of Economics and Business, LICOS Centre for Institutions and Economic Performance.
    6. van Bergeijk, P.A.G., 2017. "Measurement error of global production," ISS Working Papers - General Series 632, International Institute of Social Studies of Erasmus University Rotterdam (ISS), The Hague.

    More about this item

    Keywords

    Food&Beverage Industry; Consumption; Inequality; Economic Theory&Research; Statistical&Mathematical Sciences;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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